# Changelog
All notable changes to this project will be documented in this file.

The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

## [Unreleased]
### Added
- Updated governance docs
- Added a check to ensure that the metric used for early stopping exists before training commences ([#542](https://github.com/PyTorchLightning/pytorch-lightning/pull/542))
- Added `optimizer_idx` argument to `backward` hook ([#733](https://github.com/PyTorchLightning/pytorch-lightning/pull/733))
- Added `entity` argument to `WandbLogger` to be passed to `wandb.init` ([#783](https://github.com/PyTorchLightning/pytorch-lightning/pull/783))
- Added a tool for profiling training runs ([#782](https://github.com/PyTorchLightning/pytorch-lightning/pull/782))
- Improved flexibility for naming of TensorBoard logs, can now set `version` to a `str` to just save to that directory, and use `name=''` to prevent experiment-name directory ([#804](https://github.com/PyTorchLightning/pytorch-lightning/pull/804))
- Added option to specify `step` key when logging metrics ([#808](https://github.com/PyTorchLightning/pytorch-lightning/pull/808))
- Added `train_dataloader`, `val_dataloader` and `test_dataloader` arguments to `Trainer.fit()`, for alternative data parsing ([#759](https://github.com/PyTorchLightning/pytorch-lightning/pull/759))
- Added Tensor Processing Unit (TPU) support ([#868](https://github.com/PyTorchLightning/pytorch-lightning/pull/868))
- Added semantic segmentation example ([#751](https://github.com/PyTorchLightning/pytorch-lightning/pull/751),[#876](https://github.com/PyTorchLightning/pytorch-lightning/pull/876))
### Changed
- Changed default TQDM to use `tqdm.auto` for prettier outputs in IPython notebooks ([#752](https://github.com/PyTorchLightning/pytorch-lightning/pull/752))
- Changed `pytorch_lightning.logging` to `pytorch_lightning.loggers` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
- Moved the default `tqdm_dict` definition from Trainer to `LightningModule`, so it can be overridden by the user ([#749](https://github.com/PyTorchLightning/pytorch-lightning/pull/749))
### Deprecated
- None
### Removed
- Removed dependency on pandas ([#736](https://github.com/PyTorchLightning/pytorch-lightning/pull/736))
- Removed dependency on torchvision ([#797](https://github.com/PyTorchLightning/pytorch-lightning/pull/797))
- Removed dependency on scikit-learn ([#801](https://github.com/PyTorchLightning/pytorch-lightning/pull/801))
### Fixed
- Fixed a bug where early stopping `on_end_epoch` would be called inconsistently when `check_val_every_n_epoch == 0` ([#743](https://github.com/PyTorchLightning/pytorch-lightning/pull/743))
- Fixed a bug where the model checkpointer didn't write to the same directory as the logger ([#771](https://github.com/PyTorchLightning/pytorch-lightning/pull/771))
- Fixed a bug where the `TensorBoardLogger` class would create an additional empty log file during fitting ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777))
- Fixed a bug where `global_step` was advanced incorrectly when using `accumulate_grad_batches > 1` ([#832](https://github.com/PyTorchLightning/pytorch-lightning/pull/832))

## [0.6.0] - 2020-01-21
### Added
- Added support for resuming from a specific checkpoint via `resume_from_checkpoint` argument ([#516](https://github.com/PyTorchLightning/pytorch-lightning/pull/516))
- Added support for `ReduceLROnPlateau` scheduler ([#320](https://github.com/PyTorchLightning/pytorch-lightning/pull/320))
- Added support for Apex mode `O2` in conjunction with Data Parallel ([#493](https://github.com/PyTorchLightning/pytorch-lightning/pull/493))
- Added option (`save_top_k`) to save the top k models in the `ModelCheckpoint` class ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128))
- Added `on_train_start` and `on_train_end` hooks to `ModelHooks` ([#598](https://github.com/PyTorchLightning/pytorch-lightning/pull/598))
- Added `TensorBoardLogger` ([#607](https://github.com/PyTorchLightning/pytorch-lightning/pull/607))
- Added support for weight summary of model with multiple inputs ([#543](https://github.com/PyTorchLightning/pytorch-lightning/pull/543))
- Added `map_location` argument to `load_from_metrics` and `load_from_checkpoint` ([#625](https://github.com/PyTorchLightning/pytorch-lightning/pull/625))
- Added option to disable validation by setting `val_percent_check=0` ([#649](https://github.com/PyTorchLightning/pytorch-lightning/pull/649))
- Added `NeptuneLogger` class ([#648](https://github.com/PyTorchLightning/pytorch-lightning/pull/648))
- Added `WandbLogger` class ([#627](https://github.com/PyTorchLightning/pytorch-lightning/pull/627))
### Changed
- Changed the default progress bar to print to stdout instead of stderr ([#531](https://github.com/PyTorchLightning/pytorch-lightning/pull/531))
- Renamed `step_idx` to `step`, `epoch_idx` to `epoch`, `max_num_epochs` to `max_epochs` and `min_num_epochs` to `min_epochs` ([#589](https://github.com/PyTorchLightning/pytorch-lightning/pull/589))
- Renamed `total_batch_nb` to `total_batches`, `nb_val_batches` to `num_val_batches`, `nb_training_batches` to `num_training_batches`, `max_nb_epochs` to `max_epochs`, `min_nb_epochs` to `min_epochs`, `nb_test_batches` to `num_test_batches`, and `nb_val_batches` to `num_val_batches` ([#567](https://github.com/PyTorchLightning/pytorch-lightning/pull/567))
- Changed gradient logging to use parameter names instead of indexes ([#660](https://github.com/PyTorchLightning/pytorch-lightning/pull/660))
- Changed the default logger to `TensorBoardLogger` ([#609](https://github.com/PyTorchLightning/pytorch-lightning/pull/609))
- Changed the directory for tensorboard logging to be the same as model checkpointing ([#706](https://github.com/PyTorchLightning/pytorch-lightning/pull/706))
### Deprecated
- Deprecated `max_nb_epochs` and `min_nb_epochs` ([#567](https://github.com/PyTorchLightning/pytorch-lightning/pull/567))
- Deprecated the `on_sanity_check_start` hook in `ModelHooks` ([#598](https://github.com/PyTorchLightning/pytorch-lightning/pull/598))
### Removed
- Removed the `save_best_only` argument from `ModelCheckpoint`, use `save_top_k=1` instead ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128))
### Fixed
- Fixed a bug which ocurred when using Adagrad with cuda ([#554](https://github.com/PyTorchLightning/pytorch-lightning/pull/554))
- Fixed a bug where training would be on the GPU despite setting `gpus=0` or `gpus=[]` ([#561](https://github.com/PyTorchLightning/pytorch-lightning/pull/561))
- Fixed an error with `print_nan_gradients` when some parameters do not require gradient ([#579](https://github.com/PyTorchLightning/pytorch-lightning/pull/579))
- Fixed a bug where the progress bar would show an incorrect number of total steps during the validation sanity check when using multiple validation data loaders ([#597](https://github.com/PyTorchLightning/pytorch-lightning/pull/597))
- Fixed support for PyTorch 1.1.0 ([#552](https://github.com/PyTorchLightning/pytorch-lightning/pull/552))
- Fixed an issue with early stopping when using a `val_check_interval < 1.0` in `Trainer` ([#492](https://github.com/PyTorchLightning/pytorch-lightning/pull/492))
- Fixed bugs relating to the `CometLogger` object that would cause it to not work properly ([#481](https://github.com/PyTorchLightning/pytorch-lightning/pull/481))
- Fixed a bug that would occur when returning `-1` from `on_batch_start` following an early exit or when the batch was `None` ([#509](https://github.com/PyTorchLightning/pytorch-lightning/pull/509))
- Fixed a potential race condition with several processes trying to create checkpoint directories ([#530](https://github.com/PyTorchLightning/pytorch-lightning/pull/530))
- Fixed a bug where batch 'segments' would remain on the GPU when using `truncated_bptt > 1` ([#532](https://github.com/PyTorchLightning/pytorch-lightning/pull/532))
- Fixed a bug when using `IterableDataset` ([#547](https://github.com/PyTorchLightning/pytorch-lightning/pull/547))
- Fixed a bug where `.item` was called on non-tensor objects ([#602](https://github.com/PyTorchLightning/pytorch-lightning/pull/602))
- Fixed a bug where `Trainer.train` would crash on an uninitialized variable if the trainer was run after resuming from a checkpoint that was already at `max_epochs` ([#608](https://github.com/PyTorchLightning/pytorch-lightning/pull/608))
- Fixed a bug where early stopping would begin two epochs early ([#617](https://github.com/PyTorchLightning/pytorch-lightning/pull/617))
- Fixed a bug where `num_training_batches` and `num_test_batches` would sometimes be rounded down to zero ([#649](https://github.com/PyTorchLightning/pytorch-lightning/pull/649))
- Fixed a bug where an additional batch would be processed when manually setting `num_training_batches` ([#653](https://github.com/PyTorchLightning/pytorch-lightning/pull/653))
- Fixed a bug when batches did not have a `.copy` method ([#701](https://github.com/PyTorchLightning/pytorch-lightning/pull/701))
- Fixed a bug when using `log_gpu_memory=True` in Python 3.6 ([#715](https://github.com/PyTorchLightning/pytorch-lightning/pull/715))
- Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints ([#689](https://github.com/PyTorchLightning/pytorch-lightning/pull/689))
- Fixed a bug where `on_train_end` was not called when ealy stopping ([#723](https://github.com/PyTorchLightning/pytorch-lightning/pull/723))

## [0.5.3] - 2019-11-06
### Added
- Added option to disable default logger, checkpointer, and early stopping by passing `logger=False`, `checkpoint_callback=False` and `early_stop_callback=False` respectively
- Added `CometLogger` for use with Comet.ml
- Added `val_check_interval` argument to `Trainer` allowing validition to be performed at every given number of batches
- Added functionality to save and load hyperparameters using the standard checkpoint mechanism
- Added call to `torch.cuda.empty_cache` before training starts
- Added option for user to override the call t `backward`
- Added support for truncated backprop through time via the `truncated_bptt_steps` argument in `Trainer` 
- Added option to operate on all outputs from `training_step` in DDP2
- Added a hook for modifying DDP init
- Added a hook for modifying Apex
### Changed
- Changed experiment version to be padded with zeros (e.g. `/dir/version_9` becomes `/dir/version_0009`)
- Changed callback metrics to include any metrics given in logs or progress bar
- Changed the default for `save_best_only` in `ModelCheckpoint` to `True`
- Added `tng_data_loader` for backwards compatibility
- Renamed `MLFlowLogger.client` to `MLFlowLogger.experiment` for consistency
- Moved `global_step` increment to happen after the batch has been processed
- Changed weights restore to first attempt HPC weights before restoring normally, preventing both weights being restored and running out of memory
- Changed progress bar functionality to add multiple progress bars for train/val/test
- Changed calls to `print` to use `logging` instead
### Deprecated
- Deprecated `tng_dataloader`
### Removed
- None
### Fixed
- Fixed an issue where the number of batches was off by one during training
- Fixed a bug that occured when setting a ckeckpoint callback and `early_stop_callback=False`
- Fixed an error when importing CometLogger
- Fixed a bug where the `gpus` argument had some unexpected behaviour
- Fixed a bug where the computed total number of batches was sometimes incorrect
- Fixed a bug where the progress bar would sometimes not show the total number of batches in test mode
- Fixed a bug when using the `log_gpu_memory='min_max'` option in `Trainer`
- Fixed a bug where checkpointing would sometimes erase the current directory

## [0.5.2] - 2019-10-10
### Added
- Added `weights_summary` argument to `Trainer` to be set to `full` (full summary), `top` (just top level modules) or other
- Added `tags` argument to `MLFlowLogger`
### Changed
- Changed default for `amp_level` to `O1`
### Deprecated
- None
### Removed
- Removed the `print_weights_summary` argument from `Trainer`
### Fixed
- Fixed a bug where logs were not written properly
- Fixed a bug where `logger.finalize` wasn't called after training is complete
- Fixed callback metric errors in DDP
- Fixed a bug where `TestTubeLogger` didn't log to the correct directory

## [0.5.1] - 2019-10-05
### Added
- Added the `LightningLoggerBase` class for experiment loggers
- Added `MLFlowLogger` for logging with `mlflow`
- Added `TestTubeLogger` for logging with `test_tube`
- Added a different implementation of DDP (`distributed_backed='ddp2'`) where every node has one model using all GPUs
- Added support for optimisers which require a closure (e.g. LBFGS)
- Added automatic `MASTER_PORT` defualt for DDP when not set manually
- Added new GPU memory logging options `'min_max'` (log only the min/max utilization) and `'all'` (log all the GPU memory)
### Changed
- Changed schedulers to always be called with the current epoch
- Changed `test_tube` to an optional dependency
- Changed data loaders to internally use a getter instead of a python property
- Disabled auto GPU loading when restoring weights to prevent out of memory errors
- Changed logging, early stopping and checkpointing to occur by default
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug with samplers that do not specify `set_epoch`
- Fixed a bug when using the `MLFlowLogger` with unsupported data types, this will now raise a warning
- Fixed a bug where gradient norms were alwasy zero using `track_grad_norm`
- Fixed a bug which causes a crash when logging memory

## [0.5.0] - 2019-09-26
### Added
- None
### Changed
- Changed `data_batch` argument to `batch` throughout
- Changed `batch_i` argument to `batch_idx` throughout
- Changed `tng_dataloader` method to `train_dataloader`
- Changed `on_tng_metrics` method to `on_training_metrics`
- Changed `gradient_clip` argument to `gradient_clip_val`
- Changed `add_log_row_interval` to `row_log_interval`
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug with tensorboard logging in multi-gpu setup

## [0.4.9] - 2019-09-16
### Added
- Added the flag `log_gpu_memory` to `Trainer` to deactivate logging of GPU
memory utilization
- Added SLURM resubmit functionality (port from test-tube)
- Added optional weight_save_path to trainer to remove the need for a checkpoint_callback when using cluster training
- Added option to use single gpu per node with `DistributedDataParallel`
### Changed
- Changed functionality of `validation_end` and `test_end` with multiple dataloaders to be given all of the dataloaders at once rather than in seperate calls
- Changed print_nan_grads to only print the parameter value and gradients when they contain NaN
- Changed gpu API to take integers as well (e.g. `gpus=2` instead of `gpus=[0, 1]`)
- All models now loaded on to CPU to avoid device and out of memory issues in PyTorch
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug where data types that implement `.to` but not `.cuda` would not be properly moved onto the GPU
- Fixed a bug where data would not be re-shuffled every epoch when using a `DistributedSampler`

## [0.4.8] - 2019-08-31
### Added
- Added `test_step` and `test_end` methods, used when `Trainer.test` is called
- Added `GradientAccumulationScheduler` callback which can be used to schedule changes to the number of accumulation batches
- Added option to skip the validation sanity check by setting `nb_sanity_val_steps = 0`
### Changed
- None
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug when setting `nb_sanity_val_steps = 0`

## [0.4.7] - 2019-08-24
### Added
- None
### Changed
- Changed the default `val_check_interval` to `1.0`
- Changed defaults for `nb_val_batches`, `nb_tng_batches` and `nb_test_batches` to 0
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug where the full validation set as used despite setting `val_percent_check`
- Fixed a bug where an `Exception` was thrown when using a data set containing a single batch
- Fixed a bug where an `Exception` was thrown if no `val_dataloader` was given
- Fixed a bug where tuples were not properly transfered to the GPU
- Fixed a bug where data of a non standard type was not properly handled by the trainer
- Fixed a bug when loading data as a tuple
- Fixed a bug where `AttributeError` could be suppressed by the `Trainer`

## [0.4.6] - 2019-08-15
### Added
- Added support for data to be given as a `dict` or `list` with a single gpu
- Added support for `configure_optimizers` to return a single optimizer, two list (optimizers and schedulers), or a single list
### Changed
- None
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug where returning just an optimizer list (i.e. without schedulers) from `configure_optimizers` would throw an `Exception`

## [0.4.5] - 2019-08-13
### Added
- Added `optimizer_step` method that can be overridden to change the standard optimizer behaviour
### Changed
- None
### Deprecated
- None
### Removed
- None
### Fixed
- None

## [0.4.4] - 2019-08-12
### Added
- Added supoort for multiple validation dataloaders
- Added support for latest test-tube logger (optimised for `torch==1.2.0`)
### Changed
- `validation_step` and `val_dataloader` are now optional
- `lr_scheduler` is now activated after epoch
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug where a warning would show when using `lr_scheduler` in `torch>1.1.0`
- Fixed a bug where an `Exception` would be thrown if using `torch.DistributedDataParallel` without using a `DistributedSampler`, this now throws a `Warning` instead 

## [0.4.3] - 2019-08-10
### Added
- None
### Changed
- None
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug where accumulate gradients would scale the loss incorrectly

## [0.4.2] - 2019-08-08
### Added
- None
### Changed
- Changed install requirement to `torch==1.2.0`
### Deprecated
- None
### Removed
- None
### Fixed
- None

## [0.4.1] - 2019-08-08
### Added
- None
### Changed
- Changed install requirement to `torch==1.1.0`
### Deprecated
- None
### Removed
- None
### Fixed
- None

## [0.4.0] - 2019-08-08
### Added
- Added 16-bit support for a single GPU
- Added support for training continuation (preserves epoch, global step etc.)
### Changed
- Changed `training_step` and `validation_step`, outputs will no longer be automatically reduced
### Deprecated
- None
### Removed
- Removed need for `Experiment` object in `Trainer`
### Fixed
- Fixed issues with reducing outputs from generative models (such as images and text)

## [0.3.6.1] - 2019-07-27
### Added
- None
### Changed
- None
### Deprecated
- None
### Removed
- None
### Fixed
- Fixed a bug where `Experiment` object was not process safe, potentially causing logs to be overwritten

## [0.3.6] - 2019-07-25
### Added
- Added a decorator to do lazy data loading internally
### Changed
- None
### Deprecated
- None
### Removed
- None
### Fixed
- None